Develop and evolve generative AI applications and services using Python and LLMs, designing RAG solutions, integrating existing systems, and improving the scalability, reliability, and observability of AI products.
Applied AI Architect - R01572122
๐ Description
- Design target architectures and scalable data pipelines for enterprise AI systems.
- Architect ETL/ELT, feature stores, vector databases, knowledge layers, and AI data pipelines.
- Evaluate AI models and design integrations with enterprise platforms and systems.
- Define AI governance, compliance, privacy, security, and responsible AI controls.
- Design production environments and establish MLOps, CI/CD, monitoring, observability, and evaluation foundations.
- Partner with AI Builders and Value Engineers to move solutions from discovery and proof of concept into production.
๐ ๏ธ Requirements
- Strong experience as an AI, ML, Data, Solutions, or Enterprise Architect, with hands-on experience designing and deploying AI solutions.
- Strong understanding of Generative AI, Agentic AI, RAG, AI orchestration, and modern AI architectures.
- Experience designing enterprise data architectures, pipelines, integrations, and scalable AI platforms.
- Experience evaluating AI models and understanding the tradeoffs between commercial and open-source models, as well as RAG versus fine-tuning.
- Experience with platforms and frameworks such as Azure AI Foundry, AWS Bedrock, Google Vertex AI, LangGraph, and LangChain.
- Experience integrating AI with enterprise systems and APIs. Healthcare experience with FHIR/HL7, Epic, or Oracle is a plus, as is financial services experience with payment data and core banking platforms.
- Strong understanding of AI governance, model risk management, responsible AI, privacy, security, and regulatory compliance.
- Experience working within regulated environments, with exposure to requirements such as HIPAA, GxP/CSV, AML, Basel III, or SR 11-7 highly desirable.
- Experience with enterprise security concepts including PHI/PII governance, data residency, VPC deployment, IAM, access control, and model inference security.
- Experience with production AI engineering, including CI/CD, MLOps, model registries, monitoring, observability, and evaluation frameworks.
Full job description
Applied AI Architect (Data, Systems & Governance) | Brillio
Architect the Foundations of Enterprise AI
Brillio is looking for an Applied AI Architect who can turn ambitious AI strategies into secure, scalable, enterprise-ready systems.
You'll help determine how AI solutions should be designed, what models should power them, how enterprise data should flow through them, and how they can operate safely within highly regulated environments.
About Brillio
Brillio is one of the fastest-growing digital technology service providers, helping Fortune 1000 organizations turn disruption into competitive advantage through innovative digital adoption.
Our professionals combine deep technology expertise, design thinking, and a strong commitment to client success to deliver transformative outcomes.
Brillio is recognized year after year as a Great Place to Workยฎ, giving talented technology professionals the opportunity to work with cutting-edge technologies and solve complex, high-impact business challenges.
The Role
As an Applied AI Architect, you'll own the technical foundations that make enterprise AI possible.
You'll bring together data architecture, AI/ML systems, cloud platforms, enterprise integration, security, privacy, and governance to design solutions that can move beyond experimentation and into production.
Role Responsibilities:
- Design target architectures and scalable data pipelines for enterprise AI systems
- Architect ETL/ELT, feature stores, vector databases, knowledge layers, and AI data pipelines
- Evaluate and select AI models based on use case, performance, cost, latency, security, and governance requirements
- Design AI solutions that integrate with enterprise platforms including EHRs, core banking systems, CRM platforms, data lakes, and data warehouses
- Define and implement AI governance, compliance, privacy, and responsible AI frameworks
- Translate regulatory requirements into concrete technical and architectural controls
- Embed security, privacy, IAM, and data residency requirements from day one
- Design production environments across platforms such as Azure AI Foundry, AWS Bedrock, and Google Vertex AI
- Partner closely with AI Builders and Value Engineers throughout the lifecycle โ from discovery through production
- Establish the foundations for MLOps, CI/CD, model lifecycle management, observability, monitoring, and evaluation
- Help move AI solutions from proof-of-concept into secure, scalable production environments
Job Requirements:
- Strong experience as an AI, ML, Data, Solutions, or Enterprise Architect, with hands-on experience designing and deploying AI solutions.
- Strong understanding of Generative AI, Agentic AI, RAG, AI orchestration, and modern AI architectures.
- Experience designing enterprise data architectures, pipelines, integrations, and scalable AI platforms.
- Experience evaluating AI models and understanding the tradeoffs between commercial and open-source models, as well as RAG versus fine-tuning.
- Experience with platforms and frameworks such as Azure AI Foundry, AWS Bedrock, Google Vertex AI, LangGraph, and LangChain.
- Experience integrating AI with enterprise systems and APIs. Healthcare experience with FHIR/HL7, Epic, or Oracle is a plus, as is financial services experience with payment data and core banking platforms.
- Strong understanding of AI governance, model risk management, responsible AI, privacy, security, and regulatory compliance.
- Experience working within regulated environments, with exposure to requirements such as HIPAA, GxP/CSV, AML, Basel III, or SR 11-7 highly desirable.
- Experience with enterprise security concepts including PHI/PII governance, data residency, VPC deployment, IAM, access control, and model inference security.
- Experience with production AI engineering, including CI/CD, MLOps, model registries, monitoring, observability, and evaluation frameworks.
- Strong communication skills and the ability to translate complex technical, business, security, and regulatory requirements into clear architectural decisions.
If you're an architect who understands both the possibilities of AI and the realities of enterprise technology, security, data, and regulation, this is an opportunity to have a major impact.
Join Brillio and help build the foundations for enterprise AI.
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